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arXiv · 2101.10936

Combining Particle Swarm Optimizer with SQP Local Search for Constrained Optimization Problems

Carwyn Pelley·Mauro S. Innocente·Johann Sienz
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Abstract

The combining of a General-Purpose Particle Swarm Optimizer (GP-PSO) with Sequential Quadratic Programming (SQP) algorithm for constrained optimization problems has been shown to be highly beneficial to the refinement, and in some cases, the success of finding a global optimum solution. It is shown that the likely difference between leading algorithms are in their local search ability. A comparison with other leading optimizers on the tested benchmark suite, indicate the hybrid GP-PSO with implemented local search to compete along side other leading PSO algorithms.

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Carwyn Pelley, Mauro S. Innocente, Johann Sienz. 2021-01-25. Combining Particle Swarm Optimizer with SQP Local Search for Constrained Optimization Problems. https://arxiv.org/abs/2101.10936

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